Algorithms for clustering data
Algorithms for clustering data
ACM Computing Surveys (CSUR)
Using the fractal dimension to cluster datasets
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Data mining: concepts and techniques
Data mining: concepts and techniques
Information Retrieval
Clustering Algorithms
Stability-Based Model Order Selection in Clustering with Applications to Gene Expression Data
ICANN '02 Proceedings of the International Conference on Artificial Neural Networks
Fast and Robust Smallest Enclosing Balls
ESA '99 Proceedings of the 7th Annual European Symposium on Algorithms
Streaming-Data Algorithms for High-Quality Clustering
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
Comparing clusterings: an axiomatic view
ICML '05 Proceedings of the 22nd international conference on Machine learning
Elements of Information Theory (Wiley Series in Telecommunications and Signal Processing)
Elements of Information Theory (Wiley Series in Telecommunications and Signal Processing)
An objective approach to cluster validation
Pattern Recognition Letters
Adaptive non-linear clustering in data streams
CIKM '06 Proceedings of the 15th ACM international conference on Information and knowledge management
Model-based evaluation of clustering validation measures
Pattern Recognition
Density-based clustering for real-time stream data
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
A framework for clustering evolving data streams
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
A density-based cluster validity approach using multi-representatives
Pattern Recognition Letters
A comprehensive validity index for clustering
Intelligent Data Analysis
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
New ensemble methods for evolving data streams
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Adapting the right measures for K-means clustering
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Self-Adaptive Anytime Stream Clustering
ICDM '09 Proceedings of the 2009 Ninth IEEE International Conference on Data Mining
The Journal of Machine Learning Research
New approach in data stream association rule mining based on graph structure
ICDM'10 Proceedings of the 10th industrial conference on Advances in data mining: applications and theoretical aspects
ICDMW '10 Proceedings of the 2010 IEEE International Conference on Data Mining Workshops
Understanding of Internal Clustering Validation Measures
ICDM '10 Proceedings of the 2010 IEEE International Conference on Data Mining
iVAT and aVAT: enhanced visual analysis for cluster tendency assessment
PAKDD'10 Proceedings of the 14th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I
MOA: a real-time analytics open source framework
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part III
Tracing Evolving Subspace Clusters in Temporal Climate Data
Data Mining and Knowledge Discovery
AnyOut: anytime outlier detection on streaming data
DASFAA'12 Proceedings of the 17th international conference on Database Systems for Advanced Applications - Volume Part I
Stream data mining using the MOA framework
DASFAA'12 Proceedings of the 17th international conference on Database Systems for Advanced Applications - Volume Part II
Neighborhood-Based smoothing of external cluster validity measures
PAKDD'12 Proceedings of the 16th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I
Mining of temporal coherent subspace clusters in multivariate time series databases
PAKDD'12 Proceedings of the 16th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I
Data stream clustering: A survey
ACM Computing Surveys (CSUR)
Online fuzzy medoid based clustering algorithms
Neurocomputing
| Hi-index | 0.00 |
Due to the ever growing presence of data streams, there has been a considerable amount of research on stream mining algorithms. While many algorithms have been introduced that tackle the problem of clustering on evolving data streams, hardly any attention has been paid to appropriate evaluation measures. Measures developed for static scenarios, namely structural measures and ground-truth-based measures, cannot correctly reflect errors attributable to emerging, splitting, or moving clusters. These situations are inherent to the streaming context due to the dynamic changes in the data distribution. In this paper we develop a novel evaluation measure for stream clustering called Cluster Mapping Measure (CMM). CMM effectively indicates different types of errors by taking the important properties of evolving data streams into account. We show in extensive experiments on real and synthetic data that CMM is a robust measure for stream clustering evaluation.